Prevalence and Risk Factors for Non-HACEK Gram-negative Bacillus Endocarditis: A Retrospective Analysis
Bibliographic record
Abstract
BACKGROUND: Infective endocarditis (IE) caused by non-HACEK gram-negative bacilli (GNB) is challenging to diagnose and is associated with high morbidity and mortality. There is growing literature highlighting the differences in IE prevalence amongst various GNB; therefore, there is an impetus to characterize these traditionally "atypical" organisms to accurately risk stratify this complex infectious diagnosis. METHODS: This investigation was a retrospective cohort study conducted at 2 health care institutions in the USA. All hospitalized adult patients with bloodstream infection with a prespecified GNB (Enterobacter cloacae, Escherichia coli, Klebsiella aerogenes, Klebsiella pneumoniae, Klebsiella oxytoca, Pseudomonas aeruginosa, and Serratia marcescens) between January 1, 2018 to December 31, 2022, were included. Chart review was performed to collect clinical information, IE risk factors, and diagnosis of IE. RESULTS: Among 6678 patients with GNB bacteremia, 82 (1.2%) developed IE. The prevalence of IE among patients with Serratia marcescens or Pseudomonas aeruginosa bacteremia was higher than that with Enterobacteriaceae, with increased risk amongst patients with a history of injection drug use (43.1% vs 9.3%) and prosthetic valves (19.5% vs 2.2%). There was no statistically significant species variation in IE prevalence when endocardial device was present. Importantly, we found discordance in clinical practice in the use of diagnostic echocardiography in patients with a high risk of GNB IE. CONCLUSIONS: Serratia marcescens and Pseudomonas aeruginosa have a higher prevalence of IE compared with Enterobacteriaceae, with the differential risk most pronounced among patients with a history of injection drug use and prosthetic valves. This highlights the opportunity to optimize the diagnosis of IE amongst patients with GNB bacteremia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".